Skepticism toward Autonomous Vehicle's (AVs') ability to coexist in our transportation system in a safe, effective, and desirable manner has been a momentous barrier to the market deployment of AVs. A critical element in the development and deployment of AVs is the design of car-following (CF) controllers capable of producing desirable performance in real-world settings. Ideally, a CF control system would effectively and safely handle the longitudinal maneuvers of the vehicle at every encounter it faces. However, designing and training such a controller requires enormous data, testing, and experimentation that covers all possible driving scenarios/encounters. In other words, it requires us to have a perfect understanding of the environment these AVs would be operating under. Clearly, this is very challenging and, possibly, unattainable. AVs are likely to encounter unseen scenarios and be exposed to exogenous and endogenous uncertainties in the physical world. The sources of exogenous and endogenous uncertainties are vast and roughly classified into [1]}, [2]}, [3]}: (i) vehicular and system dynamics (e.g., vehicle condition, road gradient, aerodynamic drag force, external loads, transmission, brake, the performance of the engine, etc.), (ii) environmental conditions (snow, dust, wind, wet conditions, etc.), and (iii) situational detection (e.g., sensor/measurement errors, radar errors, vehicle speed fluctuations, vehicle localization, communication latency, etc.). All these types of uncertainties can hinder desirable performance (e.g., stability). Yet, a major challenge lies in the complexity of integrating these uncertainties into the control system and the design of the AV. For instance, it is often hard to formulate an analytical representation of these uncertainties and quantify their impact on performance.
Additionally, even when these uncertainties can be modeled through parameterization, the complexity of formulation increases significantly. The caveat, however, is that such uncertainties are most influential on the CF control as they can alter the desired performance, resulting in actuation lag and a mismatch between demanded acceleration and realized acceleration of the AV. Such behavior has been shown to impact the local/string stability of the vehicle and the traffic system as a whole [2]}, [5]}, [6]}, [7]}.
